Ryan Sullivan

Claude Cowork, Mythos, and the Future of Software: my conversation with @felixrieseberg, who leads Cowork at @AnthropicAI 00:00 Intro 01:53 Claude Mythos Preview and the “step-function change” 06:16 Why Anthropic is treating Mythos differently 11:19 The real story behind Claude Cowork’s “10-day” build 12:42 Why Anthropic realized Claude Code needed a non-technical version 15:44 What Claude Cowork actually is 17:03 Under the hood: virtual machines, tools, skills 18:36 Where Cowork’s memory actually lives 19:26 How Cowork connects to files, apps, and the internet 20:45 Why Felix thinks the local computer is under-appreciated 24:49 Trust: how do you get users comfortable with AI agents? 28:45 What UX actually means for AI agents 31:27 Anthropic Cowork's roadmap is only one month long 34:12 Building 100 prototypes 35:10 If execution is free, what becomes the bottleneck? 37:25 Does it come down to taste? 40:12 The hardest part of building Claude Cowork 41:43 Advice for founders building AI agents 44:21 SaaSpocalypse: what’s left for software startups 49:30 Where AI agents are going next 51:20 Regulated industries and enterprise adoption 54:15 Hot takes: what's underrated, overrated, and what Felix would build today

Vibe coding prototypes is cool Building production-grade, secure apps with AI you can actually run a business on… that’s a different ballgame Huge launch day for @softr_io, crazy shipping velocity over the last year 🔥@mariam_hakobyan @mkrtchyanartur

Thrilled to announce that @FirstMarkCap has led the Series A in @tracebit_com , the company building the category-defining assume breach platform for the AI era, and I am joining the board. Our thesis is simple: prevention and zero trust alone are no longer enough. AI is expanding the attack surface faster than any team can defend it, and the smartest CISOs have stopped asking 'how do I keep attackers out' and started asking 'how do I catch them the moment they're in, before they can do any damage?' That's exactly what Tracebit does, and we couldn't be more excited to partner with Andy, Sam Cox and the rest of the team. cc @Accel / @algovc, @TapestryVC, @MMC_Ventures, and CCL.

Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of @LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack 00:00 Intro - meet @hwchase17 - at the Chase Center for the @daytonaio Compute conference 01:32 What changed in agents over the last year 03:57 Why coding agents are ahead 06:26 Do models commoditize the framework layer? 08:27 Harnesses, in plain English 10:11 Why system prompts matter so much 13:11 The upside — and downside — of subagents 15:31 Why a useful agent needs a filesystem 18:13 Additional core primitives of modern agents 19:12 Skills: the new primitive 20:19 What context compaction actually means 23:02 How memory works in agents 25:16 One mega-agent or many specialized agents? 27:46 The future of MCP 29:38 Why agents need sandboxes 32:35 How sandboxes help with security 33:32 How Harrison Chase started LangChain 37:24 LangChain vs LangGraph vs Deep Agents 40:17 Why observability matters more for agents 41:48 Evals, no-code, and continuous improvement 44:41 What LangChain is building next 45:29 Where the real moat in AI lives

Thrilled to partner with @MaxJunestrand, @siggelabor, @davidneckstein, Pat, Jacob and the entire @WeAreLegora team as they build the legal operating system for the AI era! Legal work demands judgment, precision, and trust. What’s exciting about this moment is that AI can actually support that work in a meaningful way, helping legal teams move faster, navigate complexity, and deliver high-quality work without compromising rigor. That’s a big part of why we’re so excited about Legora. They’re building the leading platform around how legal work actually gets done.

Did you know there's a ChatGPT-like system where other humans answer your questions instead of AI? Surprisingly, doesn't work super well...

Voice used to be AI’s forgotten modality - now it's having its big moment: rapid innovation, big funding rounds, major agentic applications My conversation with @neilzegh, top AI researcher in the field (@GoogleDeepMind, @Meta, @kyutai_labs) and now CEO of @GradiumAI This is a reference episode on all things voice AI 🔥 00:00 Intro 01:21 Voice AI’s big moment, and why we’re still early 03:34 Why voice lagged behind text/image/video 06:06 The convergence era: transformers for every modality 07:40 Beyond Her: always-on assistants, wake words, voice-first devices 11:01 Voice vs text: where voice fits (even for coding) 12:56 Neil’s origin story: from finance to machine learning, with help from @ylecun and @soumithchintala 18:35 Neural codecs (SoundStream): compression as the unlock 22:30 Kyutai: open research, small elite teams, moving fast 31:32 Why big labs haven’t “won” voice AI4 34:01 On-device voice: where it works, why compact models matter 46:37 The last mile: real-world robustness, pronunciation, uptime 41:35 Benchmarking voice: why metrics fail, how they actually test 47:03 Cascades vs speech-to-speech: trade-offs + what’s next 54:05 Hardest frontier: noisy rooms, factories, multi-speaker chaos 1:00:50 New languages + dialects: what transfers, what doesn’t 1:02:54 Hardware & compute: why voice isn’t a 10,000-GPU game 1:07:27 What data do you need to train voice models 1:09:02 Deepfakes + privacy: why watermarking isn’t a solution 1:12:30 Voice + vision: multimodality, screen awareness, video+audio 1:14:43 Voice cloning vs voice design: where the market goes 1:16:32 Paris/Europe AI: talent density, underdog energy, what’s next

@alvinalaphat @will__ye accidentally left opus temperature=1

@sullivan @will__ye I was unaware of your game ryan

VCs are getting funnier

Lots of chatter around which models are winning the enterprise war We asked 150+ CTOs / CPOs in our @FirstMarkCap Guilds (mix of F1000 + private unicorns) what they use. Small curated sample that's very enterprise skewed, admittedly This isn't "the market," but it is what the moment in time standard looks to be inside large co's: - OpenAI leads on primary API usage - Gemini and Anthropic have gained real ground YoY - "Other" has collapsed as the large labs have consolidated spend - 58%+ of respondents use 2+ model APIs across their org Bigger takeaway: ask different audiences, get different answers. Hackers, startups, MM co's, and global enterprises are operating under different parameters (security, procurement, data residency, multi-model strategy, etc)

Excited to be leading @VillageSQL $25M Series A alongside @GVteam , @sparkcapital & @homebrew . @VillageSQL is reigniting innovation in MySQL 🔥 - bringing permissionless extensions & AI-native functionality inside the database, without migrations. Big moment for the MySQL ecosystem. #AI #Databases #OpenSource #MySQL #VentureCapital firstmark.com/story/why-we-i…

NVIDIA's New Moat & Why China is "Semiconductor Pilled” — loved my conversation with Dylan Patel (@dylan522p). Raw, incredibly insightful, and... hilarious. 00:00 - Intro 01:16 - Nvidia acquires Groq: A pivot to specialization 07:09 - Why AI models might need "wide" compute, not just fast 10:06 - Is the CUDA moat dead? (Open source vs. Nvidia) 17:49 - The startup landscape: Etched, Cerebras, and 1% odds 22:51 - Geopolitics: China's "semiconductor-pilled" culture 35:46 - Huawei's vertical integration is terrifying 39:28 - The $100B AI revenue reality check 41:12 - US Onshoring: Why total self-sufficiency is a fantasy 44:55 - Can the US actually build fabs? (The delay problem) 48:33 - The CapEx Bubble: Is $500B spending irrational? 54:53 - Energy Crisis: Why gas turbines will power AI, not nuclear 57:06 - The "AI uses all the water" myth (Hamburger comparison) 1:03:40 - Circular Debt? Debunking the Nvidia-CoreWeave risk 1:07:24 - Claude Code & the software singularity 1:10:23 - The death of the Junior Analyst role 1:11:14 - Model predictions: Opus 4.5 and the RL gap 1:14:37 - San Francisco Lore: Living with roommates @dwarkesh_sp & @_sholtodouglas

Every human worker needs a computer. Soon, every AI agent will too, but not one: millions, provisioned instantly, securely isolated, and running in parallel. Thrilled to be partnering with @ivanburazin (who didn't a break during the holidays), @JukicVedran and @daytonaio (+ @upfrontvc, @datadoghq, @figma, @PaceCap) mattturck.com/daytona

Very excited to announce our $20m Series A in @porterdotrun, led by the fantastic Justin Rhee (@rheejust). Porter was built to make infrastructure something startups don’t have to relearn as they scale, delivering a full PaaS experience inside their customer’s own cloud accounts. Porter has quickly become the default choice for this generation of AI companies (cc @TennrOfficial, @outtake_ai, @ModelML_, @GigaAI), already a top-five all time most used dev tool in YC, but impressively touts public companies and $1bn+ revenue businesses as customers as well. Their product roadmap is endless and we couldn’t be more excited to be on the journey with Justin and team.

NYC Angel Investors! FirstMark, @alley_corp, @usv, @LererHippeau, and @InspiredCap are hosting the inaugural NYC Angels Summit, bringing together a select group of the most active and up-and-coming angels investing in the NYC ecosystem. Apply to attend at the link below ⬇️

Gemini 3, scaling laws and the 'finite data' era: my conversation with @borgeaud_s, research engineer at @GoogleDeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – @OriolVinyalsML's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing

A major new entrant in voice AI: @GradiumAI If we were designing computers from scratch today, the default interface probably wouldn’t be a keyboard. It would be voice. Voice is the most natural interface we have, and probably the most underserved modality in AI today. We’re excited to announce that @FirstMarkCap is co-leading Gradium’s $70M seed round with Eurazeo alongside DST Global, Eric Schmidt, Xavier Niel, Rodolphe Saadé, Korelya, Amplify Partners and others. Gradium was founded in September 2025 by @neilzegh (Meta/Google DeepMind), @olivierteboul (Google Brain), @lmazare (Google DeepMind/Jane Street) and @honualx (Meta). Collectively they invented and open-sourced neural audio codecs and audio language models, and used this technology to power the very first voice cloning, text-to-music generation and speech-to-speech translation. They then created @kyutai_labs , a non-profit lab pushing the frontiers of multimodal LLMs, in particular releasing the first real-time conversational model in 2024. If you’re building anything voice-native – agents, games, learning tools, healthcare or something entirely new – you can start working with the product today at gradium.ai.

Thanksgiving-week treat: an epic conversation on Frontier AI with @lukaszkaiser -co-author of “Attention Is All You Need” (Transformers) and leading research scientist at @OpenAI working on GPT-5.1-era reasoning models. 00:00 – Cold open and intro 01:29 – “AI slowdown” vs a wild week of new frontier models 08:03 – Low-hanging fruit, infra, RL training and better data 11:39 – What is a reasoning model, in plain language 17:02 – Chain-of-thought and training the thinking process with RL 21:39 – Łukasz’s path: from logic and France to Google and Kurzweil 24:20 – Inside the Transformer story and what “attention” really means 28:42 – From Google Brain to OpenAI: culture, scale and GPUs 32:49 – What’s next for pre-training, GPUs and distillation 37:29 – Can we still understand these models? Circuits, sparsity and black boxes 39:42 – GPT-4 → GPT-5 → GPT-5.1: what actually changed 42:40 – Post-training, safety and teaching GPT-5.1 different tones 46:16 – How long should GPT-5.1 think? Reasoning tokens and jagged abilities 47:43 – The five-year-old’s dot puzzle that still breaks frontier models 52:22 – Generalization, child-like learning and whether reasoning is enough 53:48 – Beyond Transformers: ARC, LeCun’s ideas and multimodal bottlenecks 56:10 – GPT-5.1 Codex Max, long-running agents and compaction 1:00:06 – Will foundation models eat most apps? The translation analogy and trust 1:02:34 – What still needs to be solved, and where AI might go next

We’re growing our early-stage Data Infra, ML & AI investing team @FirstMarkCap! As proud early investors in @dataiku, @synthesiaIO, @ClickHouseDB, Pigment, @CockroachDB, @ada_cx, @hcompany_ai and many others, we're looking for an exceptional early-career Investor to help us find and partner with the next generation of iconic companies. Ideal candidates have ~2-4 years of experience in investing/operating, are deeply passionate about all things Data & AI, have a high degree of agency, and are capable of making a lasting impression with the FirstMark team and our portfolio companies. If this is you or someone you know, feel free to dm directly here or at recruiting@firstmark.com!

*Major* open source AI drop today. Can America win the Open AI race? My conversation with @natolambert and @soldni of @allen_ai about the launch of Olmo 3 00:00 – Cold Open 00:39 – Welcome & today’s big announcement 01:18 – Introducing the Olmo 3 model family 02:07 – What “base models” really are (and why they matter) 05:51 – Dolma 3: the data behind Olmo 3 08:06 – Performance vs Qwen, Gemma, DeepSeek 10:28 – What true open source means (and why it’s rare) 12:51 – Intermediate checkpoints, transparency, and why AI2 publishes everything 16:37 – Why Qwen is everywhere (including U.S. startups) 18:31 – Why Chinese labs go open source (and why U.S. labs don’t) 20:28 – Inside ATOM: the U.S. response to China’s model surge 22:13 – The rise of “thinking models” and inference-time scaling 35:58 – The full Olmo pipeline, explained simply 46:52 – Pre-training: data, scale, and avoiding catastrophic spikes 50:27 – Mid-training (tail patching) and avoiding test leakage 52:06 – Why long-context training matters 55:28 – SFT: building the foundation for reasoning 1:04:53 – Preference tuning & why DPO still works 1:10:51 – The hard part: RLVR, long reasoning chains, and infrastructure pain 1:13:59 – Why RL is so technically brutal 1:18:17 – Complexity tax vs AGI hype 1:21:58 – How everyone can contribute to the future of AI 1:27:26 – Closing thoughts